Explanation

Case Incidence

This presents the daily number of cumulative cases, and daily cases of COVID19 for selected countries around the world. The data is drawn from the European CDC, which has collected data from various governing agencies around the world. There are some small discrepancies between this data and that given by the official Australian government results, but in the absence of up to date easily obtainable data from Australia, this is the highest quality data that we have.

Cumulative Incidence for selected countries

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This graphic shows the cumulative cases of COVID19 for selected countries, against the number of days since a country exceeded 100 cumulative cases.

Note that Australia the curve is starting to flatten, as we have had fewer cases per day.

Although Australia does not have the same level of control over cases as countries like Japan and Singapore, it seems that the measures the Australian government have taken to limit travelers, and enforce physical distancing are working.

Daily Global Incidence

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The number of cases per day recorded in Australia, as provided by the European CDC.

We note that there are some days where the number of cases appears to spike upwards, followed by a decrease the following day. This indicates that there may be some data discrepancies in how the European CDC is capturing data from WHO Situation Reports. It underlines the importance of nations providing reliable machine-readable access to their own COVID-19 data. By “machine-readable” we mean CSV or JSON data files which are automatically downlaodable, or an API which can be queried automatically to yield such data. Neither of those are difficult to establish, yet nearly all national governments have failed to provide such data, leaving it to third-party agencies abd citizen-science efforts to piece together the required data in a manner that permits ongoing analysis. There is, for example, no official machine-readable source of national COVID-19 data provided by the Australian government. NSW Health is the only State or Territory government that has made any effort in that direction by providing some machine-readable data, which we leverage in the \(R_{t}\) for NSW theme (see menu above).

Daily Global Incidence (fixed y-axis scale)

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Forcing the y axis scales to be the same for all plots means that, compared to the previous plot where the y axis could change for each country, the country with the largest number cases, in this case, the USA, appears the same, and the rest of the plots appear smaller.

This give us an important context of the number of cases relative to each country. The bottom row of countries, Australia, Japan, Singapore, and New Zealand barely register.

Explanation

To understand the spread of COVID19, we use a statistic called “R0” (R-nought). This is the number of people we expect to be infected from one COVID19 infected person arriving in a population.

We are estimating R0 from the available data, and are specifically estimating the effective reproduction number (different to basic reproduction number). This reflects the current state of a population, which may include some infections.

What does R0 mean?

If R0 is 1

• One person arriving to a population could spread it to one other person. Those people could then spread it to one other person. This is easier to manage.

If R0 is 0.3

• For every 10 people infected, it would spread to 3 other people.

If R0 is 2

• Every person arriving in the population can infect 2 more people. This can quickly get out of control.

What do we want R0 to be?

• We want the R0 to be below 1, and ideally as close to 0 as possible.

Estimating Effective reproduction number

We estimate the effective reproduction number using mathematical modelling. This means that the estimated numbers are dependent upon the mathematical model, and while they are our best possible guess at R0, they may be some inaccuracies.

These estimates reflect the number of new cases we expect from an infected person as they interact with a population. The higher the number, the worse the control of the infection, and the more people will be infected. We want this number to be below 1, and preferably as close to 0 as possible.

Misconceptions / mistakes

R0 is not a rate, and has no units of time.

This means it is not a rate of infection, and does not tell us how fast an infection spreads.

Limitations

These estimates are for all countries do not account for people who bring the infection to the population by travelling. This means that we are assuming all of these infections are spread by the community. However, Australia actually has quite a large proportion of people who are travelling from overseas with COVID19. This means that these estimates might be over-estimating R0. We have explored what is possible if we improve incorporate these imported cases, where we have this data for New South Wales.

Selected Countries

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The effective R estimates for each country indicate that most countries are bringing the spread of the virus under control. However there is some wide variation. The measurement of effective R for Australia has decreased substantially, now falling below one, indicating that we have now started to control the outbreak.

Per Country

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This is the same information presented as in the previous graphic, but with each country split into its own graph. This allows us to see the trajectory of effective R.

Most countries are decreasing, but we notice that Japan and Singapore are fluctuating, due to recent outbreaks.

Per Country (ribbons)

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This shows the uncertainty around the estimate of the effective R. We notice that as time goes on, there is less uncertainty around the estimate, so we can more confident in our estimate at these later times.

Australia

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Japan

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Cool!

South Korea

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Cool!

China

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Cool!

Germany

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Cool!

Spain

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France

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Cool!

Italy

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Cool!

Singapore

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Cool!

United Kingdom

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Cool!

USA

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Cool!

Explanation

The total number of incident cases arising at timestep \(t\), \(I_t\), is the sum of the numbers of incident local (\(I_{t}^{local}\)) and imported (\(I_{t}^{imported}\)) cases,

\[ I_t = I_t^{local} + I_t^{imported} \]

It is assumed that, if imported cases exist, they can be distinguished from local cases, for instance through epidemiological investigations, so that \(I_{t}^{local}\) and \(I_{t}^{imported}\) are observed at each timestep.

\[\Lambda_t(w_s) = \sum_{s=1}^t (I_{t-s}^{local} + I_{t-s}^{imported}) w_s = \sum_{s=1}^t I_{t-s} w_s \] \[ \mathbb E(I_t^{local} | I_0, I_1, \ldots, I_{t-1}, w_s, T_t) = R_t\Lambda_t (w_s)\]

NSW – locally-acquired & overseas-acquired cases treated separately
(cases under investigation excluded)
parametric serial interval distribution

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Commentary

NSW – locally-acquired & overseas-acquired cases treated separately
(cases under investigation excluded)
serial interval distribution estimated from data

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Commentary

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NSW – adjusting for potential under-ascertainment, locally-acquired & overseas-acquired cases treated separately, (cases under investigation excluded), parametric serial interval distribution

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In these plots, the counts of incident cases with presumed local sources of infection have been inflated by a factor of 10, and the counts of cases with presumed overseas sources of infection inflated by a factor of 1.5. This mimics ten-fold under-ascertainment of locally-transmitted cases, and 50% under-ascertainment of inbound cases.

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NSW – adjusting for potential under-ascertainment, locally-acquired & overseas-acquired cases treated separately, (cases under investigation excluded), serial interval distribution estimated from data

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In these plots, the counts of incident cases with presumed local sources of infection have been inflated by a factor of 10, and the counts of cases with presumed overseas sources of infection inflated by a factor of 1.5. This mimics ten-fold under-ascertainment of locally-transmitted cases, and 50% under-ascertainment of inbound cases.

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NSW – locally-acquired & overseas-acquired cases treated separately
(cases under investigation included)
parametric serial interval distribution

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Commentary

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NSW – locally-acquired & overseas-acquired cases treated separately
(cases under investigation included)
serial interval distribution estimated from data

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Commentary

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NSW – all cases treated as locally-acquired
parametric serial interval distribution

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From data commentary blah blah blah.

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NSW – all cases treated as locally-acquired
serial interval distribution estimated from data

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From data commentary blah blah blah.

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